Overview

Dataset statistics

Number of variables24
Number of observations991346
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)< 0.1%
Total size in memory279.8 MiB
Average record size in memory295.9 B

Variable types

Categorical4
Numeric19
Boolean1

Alerts

Dataset has 26 (< 0.1%) duplicate rowsDuplicates
height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 3 other fieldsHigh correlation
waistline is highly overall correlated with weightHigh correlation
sight_left is highly overall correlated with sight_rightHigh correlation
sight_right is highly overall correlated with sight_leftHigh correlation
SBP is highly overall correlated with DBPHigh correlation
DBP is highly overall correlated with SBPHigh correlation
tot_chole is highly overall correlated with LDL_choleHigh correlation
LDL_chole is highly overall correlated with tot_choleHigh correlation
hemoglobin is highly overall correlated with height and 2 other fieldsHigh correlation
SGOT_AST is highly overall correlated with SGOT_ALTHigh correlation
SGOT_ALT is highly overall correlated with SGOT_AST and 1 other fieldsHigh correlation
gamma_GTP is highly overall correlated with SGOT_ALTHigh correlation
sex is highly overall correlated with height and 3 other fieldsHigh correlation
hear_left is highly overall correlated with hear_rightHigh correlation
hear_right is highly overall correlated with hear_leftHigh correlation
SMK_stat_type_cd is highly overall correlated with sexHigh correlation
hear_left is highly imbalanced (79.8%)Imbalance
hear_right is highly imbalanced (80.3%)Imbalance
waistline is highly skewed (γ1 = 26.78843978)Skewed
HDL_chole is highly skewed (γ1 = 104.5776351)Skewed
serum_creatinine is highly skewed (γ1 = 111.022058)Skewed
SGOT_AST is highly skewed (γ1 = 150.4916897)Skewed
SGOT_ALT is highly skewed (γ1 = 50.03887229)Skewed

Reproduction

Analysis started2023-09-16 11:19:39.461268
Analysis finished2023-09-16 11:22:50.402255
Duration3 minutes and 10.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.6 MiB
Male
526415 
Female
464931 

Length

Max length6
Median length4
Mean length4.9379793
Min length4

Characters and Unicode

Total characters4895246
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 526415
53.1%
Female 464931
46.9%

Length

2023-09-16T16:52:50.547562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:52:50.779171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 526415
53.1%
female 464931
46.9%

Most occurring characters

ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3903900
79.7%
Uppercase Letter 991346
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1456277
37.3%
a 991346
25.4%
l 991346
25.4%
m 464931
 
11.9%
Uppercase Letter
ValueCountFrequency (%)
M 526415
53.1%
F 464931
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4895246
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4895246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.614491
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:50.937922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median45
Q360
95-th percentile70
Maximum85
Range65
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.181339
Coefficient of variation (CV)0.29783662
Kurtosis-0.57561552
Mean47.614491
Median Absolute Deviation (MAD)10
Skewness0.15365339
Sum47202435
Variance201.11038
MonotonicityNot monotonic
2023-09-16T16:52:51.116717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 130385
13.2%
50 129434
13.1%
45 118355
11.9%
55 111223
11.2%
60 106063
10.7%
35 84726
8.5%
30 77600
7.8%
25 64370
6.5%
65 52961
5.3%
70 50666
 
5.1%
Other values (4) 65563
6.6%
ValueCountFrequency (%)
20 21971
 
2.2%
25 64370
6.5%
30 77600
7.8%
35 84726
8.5%
40 130385
13.2%
45 118355
11.9%
50 129434
13.1%
55 111223
11.2%
60 106063
10.7%
65 52961
5.3%
ValueCountFrequency (%)
85 3291
 
0.3%
80 14968
 
1.5%
75 25333
 
2.6%
70 50666
 
5.1%
65 52961
5.3%
60 106063
10.7%
55 111223
11.2%
50 129434
13.1%
45 118355
11.9%
40 130385
13.2%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.24063
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:51.308752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile145
Q1155
median160
Q3170
95-th percentile175
Maximum190
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.2829575
Coefficient of variation (CV)0.057217219
Kurtosis-0.53564034
Mean162.24063
Median Absolute Deviation (MAD)5
Skewness-0.02273717
Sum1.608366 × 108
Variance86.173299
MonotonicityNot monotonic
2023-09-16T16:52:51.481053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
155 165678
16.7%
150 107929
10.9%
175 98850
10.0%
145 39176
 
4.0%
180 35970
 
3.6%
140 9100
 
0.9%
185 6588
 
0.7%
Other values (3) 1690
 
0.2%
ValueCountFrequency (%)
130 86
 
< 0.1%
135 1241
 
0.1%
140 9100
 
0.9%
145 39176
 
4.0%
150 107929
10.9%
155 165678
16.7%
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
175 98850
10.0%
ValueCountFrequency (%)
190 363
 
< 0.1%
185 6588
 
0.7%
180 35970
 
3.6%
175 98850
10.0%
170 166328
16.8%
165 178228
18.0%
160 181809
18.3%
155 165678
16.7%
150 107929
10.9%
145 39176
 
4.0%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.28405
Minimum25
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:51.683106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q155
median60
Q370
95-th percentile85
Maximum140
Range115
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.514241
Coefficient of variation (CV)0.19774715
Kurtosis0.35922025
Mean63.28405
Median Absolute Deviation (MAD)10
Skewness0.5765566
Sum62736390
Variance156.60622
MonotonicityNot monotonic
2023-09-16T16:52:51.891298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
60 151134
15.2%
55 150415
15.2%
65 141241
14.2%
50 125079
12.6%
70 122281
12.3%
75 90207
9.1%
45 63047
6.4%
80 58176
 
5.9%
85 33708
 
3.4%
90 18250
 
1.8%
Other values (14) 37808
 
3.8%
ValueCountFrequency (%)
25 9
 
< 0.1%
30 157
 
< 0.1%
35 1948
 
0.2%
40 16639
 
1.7%
45 63047
6.4%
50 125079
12.6%
55 150415
15.2%
60 151134
15.2%
65 141241
14.2%
70 122281
12.3%
ValueCountFrequency (%)
140 3
 
< 0.1%
135 5
 
< 0.1%
130 43
 
< 0.1%
125 80
 
< 0.1%
120 236
 
< 0.1%
115 573
 
0.1%
110 1177
 
0.1%
105 2454
 
0.2%
100 4829
0.5%
95 9655
1.0%

waistline
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct737
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.233358
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:52.355139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile66
Q174.1
median81
Q387.8
95-th percentile97
Maximum999
Range991
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation11.850323
Coefficient of variation (CV)0.14588001
Kurtosis2066.8122
Mean81.233358
Median Absolute Deviation (MAD)6.8
Skewness26.78844
Sum80530364
Variance140.43016
MonotonicityNot monotonic
2023-09-16T16:52:52.599680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 37790
 
3.8%
81 34603
 
3.5%
82 34024
 
3.4%
84 33913
 
3.4%
86 32723
 
3.3%
83 32282
 
3.3%
76 31254
 
3.2%
78 30832
 
3.1%
85 30626
 
3.1%
79 28853
 
2.9%
Other values (727) 664446
67.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
27 1
 
< 0.1%
30 2
< 0.1%
32 3
< 0.1%
35 2
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
999 57
< 0.1%
149.1 1
 
< 0.1%
145 1
 
< 0.1%
140 1
 
< 0.1%
138 1
 
< 0.1%
136.8 1
 
< 0.1%
136 2
 
< 0.1%
135 1
 
< 0.1%
134 3
 
< 0.1%
133 1
 
< 0.1%

sight_left
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98083434
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:52.814500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60594863
Coefficient of variation (CV)0.61778897
Kurtosis144.94968
Mean0.98083434
Median Absolute Deviation (MAD)0.2
Skewness9.994626
Sum972346.2
Variance0.36717375
MonotonicityNot monotonic
2023-09-16T16:52:52.992791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 201418
20.3%
1.2 188460
19.0%
1.5 121713
12.3%
0.9 105297
10.6%
0.8 99913
10.1%
0.7 83749
8.4%
0.6 53644
 
5.4%
0.5 51895
 
5.2%
0.4 30744
 
3.1%
0.3 20388
 
2.1%
Other values (14) 34125
 
3.4%
ValueCountFrequency (%)
0.1 9503
 
1.0%
0.2 12255
 
1.2%
0.3 20388
 
2.1%
0.4 30744
 
3.1%
0.5 51895
 
5.2%
0.6 53644
 
5.4%
0.7 83749
8.4%
0.8 99913
10.1%
0.9 105297
10.6%
1 201418
20.3%
ValueCountFrequency (%)
9.9 3118
 
0.3%
2.5 7
 
< 0.1%
2.2 2
 
< 0.1%
2.1 3
 
< 0.1%
2 8452
 
0.9%
1.9 32
 
< 0.1%
1.8 25
 
< 0.1%
1.7 14
 
< 0.1%
1.6 371
 
< 0.1%
1.5 121713
12.3%

sight_right
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97842913
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:53.183257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60477411
Coefficient of variation (CV)0.61810722
Kurtosis145.92255
Mean0.97842913
Median Absolute Deviation (MAD)0.2
Skewness10.033647
Sum969961.8
Variance0.36575173
MonotonicityNot monotonic
2023-09-16T16:52:53.343940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 204493
20.6%
1.2 187266
18.9%
1.5 120620
12.2%
0.9 106186
10.7%
0.8 98777
10.0%
0.7 84168
8.5%
0.6 53238
 
5.4%
0.5 50803
 
5.1%
0.4 31318
 
3.2%
0.3 20090
 
2.0%
Other values (14) 34387
 
3.5%
ValueCountFrequency (%)
0.1 10028
 
1.0%
0.2 13002
 
1.3%
0.3 20090
 
2.0%
0.4 31318
 
3.2%
0.5 50803
 
5.1%
0.6 53238
 
5.4%
0.7 84168
8.5%
0.8 98777
10.0%
0.9 106186
10.7%
1 204493
20.6%
ValueCountFrequency (%)
9.9 3111
 
0.3%
2.5 10
 
< 0.1%
2.2 1
 
< 0.1%
2.1 10
 
< 0.1%
2 7363
 
0.7%
1.9 21
 
< 0.1%
1.8 32
 
< 0.1%
1.7 24
 
< 0.1%
1.6 390
 
< 0.1%
1.5 120620
12.2%

hear_left
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 MiB
1
960124 
2
 
31222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters991346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

Length

2023-09-16T16:52:53.507855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:52:53.698130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 991346
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 991346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 991346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 960124
96.9%
2 31222
 
3.1%

hear_right
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 MiB
1
961134 
2
 
30212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters991346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

Length

2023-09-16T16:52:53.847573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:52:54.014283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 991346
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 991346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 991346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 961134
97.0%
2 30212
 
3.0%

SBP
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.4325
Minimum67
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:54.191635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile100
Q1112
median120
Q3131
95-th percentile148
Maximum273
Range206
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.543148
Coefficient of variation (CV)0.11878503
Kurtosis0.99663922
Mean122.4325
Median Absolute Deviation (MAD)10
Skewness0.48206032
Sum1.2137297 × 108
Variance211.50315
MonotonicityNot monotonic
2023-09-16T16:52:54.412213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 78786
 
7.9%
110 72193
 
7.3%
130 71714
 
7.2%
118 40078
 
4.0%
100 30829
 
3.1%
138 24426
 
2.5%
119 24166
 
2.4%
128 23766
 
2.4%
124 22224
 
2.2%
116 22177
 
2.2%
Other values (161) 580987
58.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
70 3
 
< 0.1%
72 1
 
< 0.1%
73 4
 
< 0.1%
74 3
 
< 0.1%
75 8
< 0.1%
76 7
< 0.1%
77 6
< 0.1%
78 11
< 0.1%
79 6
< 0.1%
ValueCountFrequency (%)
273 1
< 0.1%
270 1
< 0.1%
255 1
< 0.1%
253 1
< 0.1%
244 1
< 0.1%
241 1
< 0.1%
240 1
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
235 1
< 0.1%

DBP
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.052627
Minimum32
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:54.627591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile60
Q170
median76
Q382
95-th percentile92
Maximum185
Range153
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8893654
Coefficient of variation (CV)0.13003318
Kurtosis0.89150383
Mean76.052627
Median Absolute Deviation (MAD)6
Skewness0.4000338
Sum75394468
Variance97.799547
MonotonicityNot monotonic
2023-09-16T16:52:54.850813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 123156
 
12.4%
70 111699
 
11.3%
78 44628
 
4.5%
60 41253
 
4.2%
72 33644
 
3.4%
75 32575
 
3.3%
76 31976
 
3.2%
74 31773
 
3.2%
82 27195
 
2.7%
90 25959
 
2.6%
Other values (117) 487488
49.2%
ValueCountFrequency (%)
32 1
 
< 0.1%
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
< 0.1%
37 3
 
< 0.1%
38 1
 
< 0.1%
39 3
 
< 0.1%
40 14
< 0.1%
41 7
< 0.1%
42 12
< 0.1%
ValueCountFrequency (%)
185 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
170 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
160 2
< 0.1%
156 2
< 0.1%
154 2
< 0.1%
153 2
< 0.1%

BLDS
Real number (ℝ)

Distinct498
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.42445
Minimum25
Maximum852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:55.085058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile79
Q188
median96
Q3105
95-th percentile137
Maximum852
Range827
Interquartile range (IQR)17

Descriptive statistics

Standard deviation24.17996
Coefficient of variation (CV)0.24077762
Kurtosis40.470487
Mean100.42445
Median Absolute Deviation (MAD)8
Skewness4.6173775
Sum99555374
Variance584.67045
MonotonicityNot monotonic
2023-09-16T16:52:55.302735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 35243
 
3.6%
92 35227
 
3.6%
95 35190
 
3.5%
94 35173
 
3.5%
91 34389
 
3.5%
96 33814
 
3.4%
90 33754
 
3.4%
97 32981
 
3.3%
89 32178
 
3.2%
98 31902
 
3.2%
Other values (488) 651495
65.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
33 2
< 0.1%
34 2
< 0.1%
36 2
< 0.1%
37 1
 
< 0.1%
38 4
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
852 1
< 0.1%
801 1
< 0.1%
800 1
< 0.1%
784 1
< 0.1%
769 1
< 0.1%
741 1
< 0.1%
685 1
< 0.1%
663 1
< 0.1%
638 1
< 0.1%
629 2
< 0.1%

tot_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.55702
Minimum30
Maximum2344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:55.529754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile137
Q1169
median193
Q3219
95-th percentile261
Maximum2344
Range2314
Interquartile range (IQR)50

Descriptive statistics

Standard deviation38.660155
Coefficient of variation (CV)0.19769249
Kurtosis49.462386
Mean195.55702
Median Absolute Deviation (MAD)25
Skewness1.5568817
Sum1.9386467 × 108
Variance1494.6076
MonotonicityNot monotonic
2023-09-16T16:52:55.730200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 11079
 
1.1%
184 10873
 
1.1%
189 10857
 
1.1%
190 10825
 
1.1%
188 10796
 
1.1%
197 10775
 
1.1%
192 10746
 
1.1%
187 10746
 
1.1%
196 10723
 
1.1%
186 10717
 
1.1%
Other values (464) 883209
89.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
45 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
62 1
 
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
2344 1
< 0.1%
2196 1
< 0.1%
2067 1
< 0.1%
2046 1
< 0.1%
2033 1
< 0.1%
1815 1
< 0.1%
1736 1
< 0.1%
1619 1
< 0.1%
1605 1
< 0.1%
1575 1
< 0.1%

HDL_chole
Real number (ℝ)

SKEWED 

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.9368
Minimum1
Maximum8110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:55.948817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q146
median55
Q366
95-th percentile84
Maximum8110
Range8109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.238479
Coefficient of variation (CV)0.30276515
Kurtosis48094.155
Mean56.9368
Median Absolute Deviation (MAD)10
Skewness104.57764
Sum56444069
Variance297.16516
MonotonicityNot monotonic
2023-09-16T16:52:56.176846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 29602
 
3.0%
52 28335
 
2.9%
53 28323
 
2.9%
51 28126
 
2.8%
54 27952
 
2.8%
49 27869
 
2.8%
48 27428
 
2.8%
55 27092
 
2.7%
56 26827
 
2.7%
47 26476
 
2.7%
Other values (213) 713316
72.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 7
< 0.1%
3 3
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
7 12
< 0.1%
8 6
 
< 0.1%
9 11
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
8110 1
< 0.1%
1206 1
< 0.1%
933 1
< 0.1%
797 1
< 0.1%
727 1
< 0.1%
701 1
< 0.1%
697 1
< 0.1%
677 1
< 0.1%
658 1
< 0.1%
636 1
< 0.1%

LDL_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct432
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.03769
Minimum1
Maximum5119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:56.417560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q189
median111
Q3135
95-th percentile172
Maximum5119
Range5118
Interquartile range (IQR)46

Descriptive statistics

Standard deviation35.842812
Coefficient of variation (CV)0.31708726
Kurtosis481.28298
Mean113.03769
Median Absolute Deviation (MAD)23
Skewness5.2517394
Sum1.1205946 × 108
Variance1284.7072
MonotonicityNot monotonic
2023-09-16T16:52:56.658564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 11824
 
1.2%
104 11795
 
1.2%
107 11782
 
1.2%
110 11773
 
1.2%
102 11740
 
1.2%
112 11656
 
1.2%
115 11631
 
1.2%
108 11611
 
1.2%
105 11607
 
1.2%
106 11597
 
1.2%
Other values (422) 874330
88.2%
ValueCountFrequency (%)
1 81
< 0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 11
 
< 0.1%
5 20
 
< 0.1%
6 23
 
< 0.1%
7 29
 
< 0.1%
8 40
< 0.1%
9 31
 
< 0.1%
10 39
< 0.1%
ValueCountFrequency (%)
5119 1
< 0.1%
2254 1
< 0.1%
2114 1
< 0.1%
2111 1
< 0.1%
2043 1
< 0.1%
2026 1
< 0.1%
1933 1
< 0.1%
1798 1
< 0.1%
1750 1
< 0.1%
1696 1
< 0.1%

triglyceride
Real number (ℝ)

Distinct1657
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.14175
Minimum1
Maximum9490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:56.879354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q173
median106
Q3159
95-th percentile297
Maximum9490
Range9489
Interquartile range (IQR)86

Descriptive statistics

Standard deviation102.19698
Coefficient of variation (CV)0.77338906
Kurtosis175.38524
Mean132.14175
Median Absolute Deviation (MAD)39
Skewness6.5293729
Sum1.309982 × 108
Variance10444.224
MonotonicityNot monotonic
2023-09-16T16:52:57.079820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 8236
 
0.8%
78 8207
 
0.8%
79 8178
 
0.8%
69 8139
 
0.8%
70 8131
 
0.8%
76 8122
 
0.8%
68 8120
 
0.8%
82 8102
 
0.8%
75 8096
 
0.8%
77 8095
 
0.8%
Other values (1647) 909920
91.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 10
< 0.1%
8 7
< 0.1%
9 11
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
9490 1
< 0.1%
6430 1
< 0.1%
6173 1
< 0.1%
5236 1
< 0.1%
4164 1
< 0.1%
4000 1
< 0.1%
3858 1
< 0.1%
3848 1
< 0.1%
3830 1
< 0.1%
3771 1
< 0.1%

hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.229824
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:57.282793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.7
Q113.2
median14.3
Q315.4
95-th percentile16.6
Maximum25
Range24
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5849287
Coefficient of variation (CV)0.11138077
Kurtosis0.71137942
Mean14.229824
Median Absolute Deviation (MAD)1.1
Skewness-0.3839878
Sum14106679
Variance2.5119991
MonotonicityNot monotonic
2023-09-16T16:52:57.530045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 23297
 
2.4%
14 23108
 
2.3%
13.6 23093
 
2.3%
13.4 22946
 
2.3%
13.8 22781
 
2.3%
13.3 22734
 
2.3%
13.9 22635
 
2.3%
15 22600
 
2.3%
13.7 22591
 
2.3%
14.8 22181
 
2.2%
Other values (180) 763380
77.0%
ValueCountFrequency (%)
1 3
< 0.1%
2.8 1
 
< 0.1%
3.7 3
< 0.1%
3.8 1
 
< 0.1%
3.9 3
< 0.1%
4 4
< 0.1%
4.1 2
< 0.1%
4.2 4
< 0.1%
4.3 3
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
25 2
< 0.1%
24.2 1
< 0.1%
23.9 1
< 0.1%
23.6 1
< 0.1%
23.3 1
< 0.1%
22.7 1
< 0.1%
22.1 1
< 0.1%
22 1
< 0.1%
21.8 1
< 0.1%
21.7 2
< 0.1%

urine_protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0942244
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:57.730381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43772355
Coefficient of variation (CV)0.40003087
Kurtosis36.899552
Mean1.0942244
Median Absolute Deviation (MAD)0
Skewness5.6724908
Sum1084755
Variance0.19160191
MonotonicityNot monotonic
2023-09-16T16:52:57.899557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
6 512
 
0.1%
5 1977
 
0.2%
4 6427
 
0.6%
3 16405
 
1.7%
2 30850
 
3.1%
1 935175
94.3%

serum_creatinine
Real number (ℝ)

SKEWED 

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86046668
Minimum0.1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:58.132235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.7
median0.8
Q31
95-th percentile1.2
Maximum98
Range97.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.48053042
Coefficient of variation (CV)0.55845326
Kurtosis19089.83
Mean0.86046668
Median Absolute Deviation (MAD)0.1
Skewness111.02206
Sum853020.2
Variance0.23090948
MonotonicityNot monotonic
2023-09-16T16:52:58.379173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 194902
19.7%
0.9 180626
18.2%
0.7 164293
16.6%
1 140743
14.2%
0.6 109236
11.0%
1.1 86355
8.7%
1.2 40744
 
4.1%
0.5 38932
 
3.9%
1.3 15160
 
1.5%
0.4 6050
 
0.6%
Other values (173) 14305
 
1.4%
ValueCountFrequency (%)
0.1 425
 
< 0.1%
0.2 99
 
< 0.1%
0.3 597
 
0.1%
0.4 6050
 
0.6%
0.5 38932
 
3.9%
0.6 109236
11.0%
0.7 164293
16.6%
0.8 194902
19.7%
0.9 180626
18.2%
1 140743
14.2%
ValueCountFrequency (%)
98 2
< 0.1%
96 2
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%

SGOT_AST
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct568
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.989308
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:58.624820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q119
median23
Q328
95-th percentile46
Maximum9999
Range9998
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.493386
Coefficient of variation (CV)0.90396349
Kurtosis50432.651
Mean25.989308
Median Absolute Deviation (MAD)5
Skewness150.49169
Sum25764397
Variance551.93919
MonotonicityNot monotonic
2023-09-16T16:52:58.868185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 64900
 
6.5%
21 63845
 
6.4%
19 62846
 
6.3%
22 61195
 
6.2%
18 58423
 
5.9%
23 57642
 
5.8%
24 52476
 
5.3%
17 50161
 
5.1%
25 47304
 
4.8%
26 41665
 
4.2%
Other values (558) 430889
43.5%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 19
 
< 0.1%
3 16
 
< 0.1%
4 28
 
< 0.1%
5 39
 
< 0.1%
6 70
 
< 0.1%
7 131
 
< 0.1%
8 297
 
< 0.1%
9 580
 
0.1%
10 1708
0.2%
ValueCountFrequency (%)
9999 1
< 0.1%
7000 2
< 0.1%
3742 1
< 0.1%
3440 1
< 0.1%
3235 1
< 0.1%
2670 1
< 0.1%
1962 1
< 0.1%
1911 1
< 0.1%
1870 1
< 0.1%
1686 1
< 0.1%

SGOT_ALT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct594
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.755051
Minimum1
Maximum7210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:59.109489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median20
Q329
95-th percentile59
Maximum7210
Range7209
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.308599
Coefficient of variation (CV)1.0214928
Kurtosis8615.9443
Mean25.755051
Median Absolute Deviation (MAD)7
Skewness50.038872
Sum25532167
Variance692.1424
MonotonicityNot monotonic
2023-09-16T16:52:59.568371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 47841
 
4.8%
16 47260
 
4.8%
14 47175
 
4.8%
17 46075
 
4.6%
18 44336
 
4.5%
13 44024
 
4.4%
19 41894
 
4.2%
12 40938
 
4.1%
20 38803
 
3.9%
21 36256
 
3.7%
Other values (584) 556744
56.2%
ValueCountFrequency (%)
1 31
 
< 0.1%
2 60
 
< 0.1%
3 186
 
< 0.1%
4 567
 
0.1%
5 1636
 
0.2%
6 3259
 
0.3%
7 6591
 
0.7%
8 12212
 
1.2%
9 18937
1.9%
10 31707
3.2%
ValueCountFrequency (%)
7210 1
< 0.1%
4633 1
< 0.1%
3807 1
< 0.1%
3517 1
< 0.1%
3307 1
< 0.1%
2981 1
< 0.1%
2698 1
< 0.1%
2535 1
< 0.1%
2530 1
< 0.1%
2309 1
< 0.1%

gamma_GTP
Real number (ℝ)

HIGH CORRELATION 

Distinct940
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.136347
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-09-16T16:52:59.830737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q116
median23
Q339
95-th percentile105
Maximum999
Range998
Interquartile range (IQR)23

Descriptive statistics

Standard deviation50.424153
Coefficient of variation (CV)1.3578113
Kurtosis97.042135
Mean37.136347
Median Absolute Deviation (MAD)9
Skewness7.7185093
Sum36814969
Variance2542.5952
MonotonicityNot monotonic
2023-09-16T16:53:00.069960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 41158
 
4.2%
15 40932
 
4.1%
13 39919
 
4.0%
16 39750
 
4.0%
17 37763
 
3.8%
12 36681
 
3.7%
18 35756
 
3.6%
19 33419
 
3.4%
11 31565
 
3.2%
20 31362
 
3.2%
Other values (930) 623041
62.8%
ValueCountFrequency (%)
1 16
 
< 0.1%
2 31
 
< 0.1%
3 206
 
< 0.1%
4 239
 
< 0.1%
5 621
 
0.1%
6 1623
 
0.2%
7 3493
 
0.4%
8 8202
 
0.8%
9 14181
1.4%
10 25643
2.6%
ValueCountFrequency (%)
999 239
< 0.1%
998 1
 
< 0.1%
997 1
 
< 0.1%
996 2
 
< 0.1%
994 2
 
< 0.1%
993 4
 
< 0.1%
992 2
 
< 0.1%
991 1
 
< 0.1%
990 5
 
< 0.1%
989 2
 
< 0.1%

SMK_stat_type_cd
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 MiB
1
602441 
3
213954 
2
174951 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters991346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

Length

2023-09-16T16:53:00.262718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T16:53:00.429880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

Most occurring characters

ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 991346
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 991346
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 991346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 602441
60.8%
3 213954
 
21.6%
2 174951
 
17.6%

DRK_YN
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size968.2 KiB
False
495858 
True
495488 
ValueCountFrequency (%)
False 495858
50.0%
True 495488
50.0%
2023-09-16T16:53:00.581747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-09-16T16:52:36.761329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:49.973706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:56.071824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:01.752985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:07.601404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:13.473528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:19.579357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:25.452751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:31.212472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:36.920287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:43.150092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:49.023610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:55.075710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:01.143162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:07.226810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:13.329599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:19.311197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:24.918831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:30.826505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:37.077455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:50.339043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:56.365047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:02.056986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:07.897552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:13.785462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:19.863332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:25.769007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:31.495918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:37.226311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:43.454600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:49.344121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:55.411126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:01.465071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:07.571541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:13.660852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:19.631256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:25.227913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:31.155910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:37.401502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:50.681537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:56.676523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:02.356975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:08.208209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:14.106250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:20.161019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:26.084303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:31.791166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:37.530698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:43.769351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:49.670207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:55.735834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:01.792551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:07.874516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:13.987787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:19.943672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:25.561315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:31.487931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:37.728901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:50.985710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:56.976821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:02.666931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:08.509156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:14.429249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:20.472984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:26.407707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:32.084719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:37.836160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:44.079513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:49.990257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:56.059605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:02.112738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:08.191617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:14.307195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:20.252965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:26.119992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:31.815635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:38.027455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:51.286905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:57.286005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:02.969527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:08.807004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:14.737606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:20.806885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:26.716172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:32.369799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:38.126533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:44.383276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:50.302027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:56.375754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:02.667722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:08.511327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:14.606151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:20.557348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:26.399556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:32.139318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:38.339269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:51.619111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:57.583577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:03.266339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:09.103556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:15.042667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:21.102826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:27.029112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:32.657759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:38.409012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:44.700609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:50.609089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:56.687010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:02.971808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:08.825348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:14.892534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:20.859124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:26.677127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:32.454922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:38.647972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:51.916864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:57.906228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:03.564444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:09.400777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:15.356523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:21.396533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:27.369889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:32.965162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:38.958874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:45.013557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:50.919329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:57.008457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:03.276620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:09.142384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:15.206568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:21.144456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:26.963974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:32.763474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:38.940507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:52.209011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:58.201499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:03.836652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:09.675498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:15.662727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:21.698776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:27.667943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:33.251176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:39.250270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:45.303267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:51.216608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:57.316155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:03.576154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:09.444850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:15.495810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:21.418718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:27.244958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:33.055036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:39.265748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:52.528126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:58.510092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:04.153373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:09.998985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:15.995137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:22.015516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:27.980171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:33.555334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:39.561092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:45.637978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:51.545307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:57.659072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:03.903554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:09.783014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:15.815349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:21.746865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:27.533348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:33.385979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:39.567010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:52.821517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:58.792952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:04.442142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:10.298958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:16.521019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:22.328757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:28.274657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:33.876358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:39.852026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:45.929654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:51.845787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:57.976656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:04.213682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:10.105190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:16.126846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:22.030057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:27.816160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:33.681912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:39.879325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:53.121253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:59.072617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:04.739536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:10.601505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:16.813093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:22.642446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:28.581997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:34.178388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:40.139712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:46.226598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:52.131257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:58.281140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:04.516163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:10.420132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:16.446466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:22.317021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:28.102021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:33.988141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:40.208888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:53.465631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:59.379914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:05.075023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:10.936609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:17.134967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:22.968885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:28.920928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:34.506754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:40.477908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:46.553540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:52.471211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:58.618372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:04.846076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:10.754341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:16.781410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:22.628133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:28.425932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:34.323126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:40.527013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:53.800589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:59.669146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:05.381808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:11.242990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:17.430936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:23.289056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:29.237009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:34.813200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:40.781394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:46.867329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:52.781556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:58.931669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:05.152070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:11.080275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:17.100371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:22.907673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:28.732192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:34.633194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:40.829770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:54.119013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:59.943194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:05.697534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:11.554930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:17.738216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:23.603325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:29.547732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:35.114602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:41.087346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:47.165279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:53.089417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:59.244432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:05.436533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:11.395093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:17.403258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:23.174428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:29.016670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:34.926114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:41.137276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:54.449030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:00.250269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:06.020753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:11.874880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:18.038165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:23.927064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:29.839138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:35.407269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:41.407874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:47.468206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:53.409588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:59.550273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:05.724170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:11.715640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:17.709960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:23.472118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:29.333486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:35.223238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:41.447028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:54.757887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:00.541955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:06.329767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:12.190422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:18.349088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:24.219881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:30.109310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:35.710818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:41.756081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:47.777148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:53.739631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:59.862842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:06.020310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:12.045579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:18.016908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:23.761302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:29.634385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:35.522982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:41.765986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:55.057565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:00.843357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:06.646273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:12.509376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:18.648041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:24.510752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:30.385341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:36.007965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:42.132047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:48.100136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:54.068565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:00.182243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:06.314695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:12.367510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:18.347439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:24.022635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:29.932302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:35.847419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:42.086152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:55.344891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:01.146252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:06.969020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:12.838064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:18.948725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:24.801342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:30.658075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:36.301358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:42.496529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:48.417568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:54.406233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:00.497395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:06.610225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:12.694901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:18.667564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:24.314316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:30.210741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:36.138768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:42.422873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:50:55.786587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:01.445049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:07.286578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:13.158890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:19.277376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:25.150073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:30.941249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:36.626091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:42.845923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:48.739718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:51:54.747354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:00.834129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:06.921254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:13.016615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:18.995777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:24.619975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:30.519483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T16:52:36.443989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-16T16:53:00.768325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ageheightweightwaistlinesight_leftsight_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPsexhear_lefthear_rightSMK_stat_type_cdDRK_YN
age1.000-0.385-0.1730.165-0.387-0.3800.2620.1190.2540.024-0.1140.0390.127-0.1800.0240.0010.2070.0680.0590.1270.2410.2350.1440.291
height-0.3851.0000.6800.3420.2730.2750.0560.1170.045-0.020-0.190-0.0090.1500.5850.0090.4620.0830.2470.3230.7440.0990.1000.3610.375
weight-0.1730.6801.0000.7810.1710.1720.2640.2770.2020.065-0.3480.0790.3520.5460.0250.4190.2130.4410.4620.5880.0500.0510.2820.260
waistline0.1650.3420.7811.000-0.012-0.0090.3500.3030.2860.079-0.3810.0870.4150.3940.0420.3120.2780.4530.4710.0430.0030.0030.0230.016
sight_left-0.3870.2730.171-0.0121.0000.717-0.091-0.013-0.0850.0200.0090.017-0.0080.181-0.0220.087-0.0410.0400.0440.1430.0780.0750.0700.132
sight_right-0.3800.2750.172-0.0090.7171.000-0.088-0.010-0.0840.0210.0050.018-0.0060.183-0.0230.090-0.0380.0420.0460.1470.0760.0760.0710.132
SBP0.2620.0560.2640.350-0.091-0.0881.0000.7250.2430.071-0.1410.0390.2510.1840.0400.1320.2030.2320.2780.2030.0530.0550.0910.050
DBP0.1190.1170.2770.303-0.013-0.0100.7251.0000.1920.112-0.1170.0720.2470.2490.0310.1370.1830.2320.2880.1940.0070.0080.0980.093
BLDS0.2540.0450.2020.286-0.085-0.0840.2430.1921.0000.046-0.1510.0080.2630.1320.0540.1270.1480.2280.2790.1150.0460.0470.0750.014
tot_chole0.024-0.0200.0650.0790.0200.0210.0710.1120.0461.0000.1580.8870.2750.115-0.0080.0250.1030.1260.1560.0160.0030.0050.0060.008
HDL_chole-0.114-0.190-0.348-0.3810.0090.005-0.141-0.117-0.1510.1581.000-0.042-0.469-0.241-0.024-0.227-0.104-0.249-0.2220.0010.0000.0000.0000.000
LDL_chole0.039-0.0090.0790.0870.0170.0180.0390.0720.0080.887-0.0421.0000.1090.106-0.0140.0440.0550.0900.0740.0000.0000.0030.0020.000
triglyceride0.1270.1500.3520.415-0.008-0.0060.2510.2470.2630.275-0.4690.1091.0000.2960.0300.1890.2230.3610.4490.0290.0010.0000.0240.025
hemoglobin-0.1800.5850.5460.3940.1810.1830.1840.2490.1320.115-0.2410.1060.2961.0000.0170.4580.2430.4180.4680.6210.0350.0360.3110.280
urine_protein0.0240.0090.0250.042-0.022-0.0230.0400.0310.054-0.008-0.024-0.0140.0300.0171.0000.0370.0250.0210.0380.0200.0210.0190.0140.017
serum_creatinine0.0010.4620.4190.3120.0870.0900.1320.1370.1270.025-0.2270.0440.1890.4580.0371.0000.1820.2460.3210.0080.0020.0000.0030.006
SGOT_AST0.2070.0830.2130.278-0.041-0.0380.2030.1830.1480.103-0.1040.0550.2230.2430.0250.1821.0000.7310.4630.0010.0000.0000.0010.001
SGOT_ALT0.0680.2470.4410.4530.0400.0420.2320.2320.2280.126-0.2490.0900.3610.4180.0210.2460.7311.0000.6190.0020.0000.0000.0020.000
gamma_GTP0.0590.3230.4620.4710.0440.0460.2780.2880.2790.156-0.2220.0740.4490.4680.0380.3210.4630.6191.0000.1640.0060.0070.1210.153
sex0.1270.7440.5880.0430.1430.1470.2030.1940.1150.0160.0010.0000.0290.6210.0200.0080.0010.0020.1641.0000.0030.0000.6430.369
hear_left0.2410.0990.0500.0030.0780.0760.0530.0070.0460.0030.0000.0000.0010.0350.0210.0020.0000.0000.0060.0031.0000.5370.0320.058
hear_right0.2350.1000.0510.0030.0750.0760.0550.0080.0470.0050.0000.0030.0000.0360.0190.0000.0000.0000.0070.0000.5371.0000.0310.058
SMK_stat_type_cd0.1440.3610.2820.0230.0700.0710.0910.0980.0750.0060.0000.0020.0240.3110.0140.0030.0010.0020.1210.6430.0320.0311.0000.365
DRK_YN0.2910.3750.2600.0160.1320.1320.0500.0930.0140.0080.0000.0000.0250.2800.0170.0060.0010.0000.1530.3690.0580.0580.3651.000

Missing values

2023-09-16T16:52:43.169593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T16:52:45.715653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
0Male351707590.01.01.0111208099193481269217.111.02135401Y
1Male301808089.00.91.211130821062285514812115.810.92036273N
2Male401657591.01.21.5111207098136417410415.810.94732681N
3Male501758091.01.51.21114587952017610410617.611.12934181N
4Male501656080.01.01.211138821011996111710413.810.81912251N
5Male501655575.01.21.5111429299218779523213.830.82940373Y
6Female451505569.00.50.4111015889196661157512.310.81912121N
7Male351756584.21.21.01113280941855810710114.410.81818353Y
8Male551707584.01.20.911145851042175614110015.110.83223261Y
9Male401757582.01.51.511132105100195601188313.910.92138162Y
sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
991336Male801706074.01.00.9111398310917175845712.011.21811152Y
991337Female351657081.01.01.011113698117363928813.310.72017121N
991338Male201756574.51.01.5111057087211721209215.410.82526502Y
991339Male701656078.00.90.8111377893167578910516.111.02313321Y
991340Female501505072.61.01.011116741081784810512515.210.82826291N
991341Male451758092.11.51.51111480881984612513215.011.02636271N
991342Male351707586.01.01.511119838313340844515.811.11417151N
991343Female401555068.01.00.7111107090205967715714.310.83027173Y
991344Male251756072.01.51.011119746912238735314.510.82114171N
991345Male501607090.51.01.51113379992253915316315.810.92443363Y

Duplicate rows

Most frequently occurring

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN# duplicates
0Female201605070.01.01.011106687615445985612.710.81813111N2
1Female301504560.01.20.9111006010618172909312.010.91611241N2
2Female401605567.02.01.5111208097184641028713.010.71611432Y2
3Female401708588.00.90.911120701101914712111510.410.91714331N2
4Female451656582.01.01.0111208087178641035313.610.51719281N2
5Female501557090.81.01.011150961012304315018314.910.82422421N2
6Female551405078.00.91.21113488812084412145613.810.52124271N2
7Female651455076.01.00.91115496862686516021216.210.72122251N2
8Female651505586.00.90.91112065992286213913611.910.72718141N2
9Female651555569.20.70.711130801252945218528313.830.63330241N2